1. Cascade R-CNN: Delving into High Quality Object Detection
Zhaowei Cai, Nuno Vasconcelos
University of California San Diego

2. Object Detection Definition of positive/negative samples positive??
Ambiguous!!
positive??

3. Object Detection Standard definition
is to use an intersection over union (IoU) threshold (usually 0.5).
positive √ positive √ positive × positive ×

4. Object Detection During training IoU=0.5
these samples are on opposite sides of the class boundary.
IoU=0.5

5. Object Detection However, this threshold is loose IoU=0.5
many positive samples are of low quality.
IoU=0.5
low quality positives

6. Current Object Detection
Detection results can be quite noisy!
low quality detector

7. High Quality Object Detection
Q: how to train a high quality object detector?
high quality detector

8. Low Quality Object Detection
Training with low IoU threshold
IoU=0.5

9. High Quality Object Detection
Training with high IoU threshold
IoU=0.7

10. High Quality Object Detection
Two problems 1. Training overfitting, due to exponentially vanishing positive samples. 2. Inference-time quality mismatch between the detector and its input proposals.

11. Cascade R-CNN Multi-stage framework, increasing IoU threshold
Faster R-CNN
Cascade R-CNN

12. Why Cascade R-CNN? Two benefits
IoU=0.5
IoU=0.6
IoU=0.7
Two benefits
1. Reduce training overfitting, since the proposal quality is sequentially improved.
2. Reduce Inference-time quality mismatch, because the same cascade is also used at inference.

13. Effective High Quality Detection
Large gains for high quality detection
Detection Quality

14. State-of-the-art Results
The best single model results on COCO

15. Cascade R-CNN: Delving into High Quality Object Detection
Welcome to our poster!
(P2-3-D14)
Cascade R-CNN: Delving into High Quality Object Detection